Models and Mechanisms in Cognitive Science Workshop

There is an increasing amount of interest in the distinctive role that models and mechanisms play in cognitive science (Machamer, Darden & Craver, 2001, Craver, 2007, Piccinini, 2007, Bechtel & Abrahamsen, 2005). However, more detailed investigations are needed to track the heuristic roles of models and mechanisms, how these approaches are related, and how they contribute to questions of explanation, reduction and scientific realism in specific cases, and in the cognitive sciences more generally. This preliminary workshop aims to explore these questions through a mix of presentations and discussions, led by Stephan Hartman (Tilburg) and Mark Sprevak (Cambridge/Edinburgh). Relevant topics include the role of models in terms of their predictive or representational functions, the constraints (if any) that models of cognition must satisfy, and if there is empirical evidence to show that particular models (e.g. the Bayesian brain, Knill & Pouget, 2004) provide accurate accounts of the brain or should be viewed instrumentally. The way that models and mechanisms are related, through one-way or mutual constraints, or through a potential transition from models to mechanisms, will also be an important area of discussion. There may also be issues specific to models and mechanisms within cognitive science that affect their relationship and their impact on discussions of explanation, reduction, and realism.

Topic examples

In comparison to other disciplines like physics or economics, is there anything special about modelling in cognitive neuroscience?

Do models in cognitive neuroscience serve mainly predictive purposes rather than representational functions?

What are the kind of considerations that can orient model-building in cognitive sciences?

How is knowledge transferred from a model to its target?

How do models relate to mechanisms?

Under what conditions do models become mechanistic?

What are the mechanistic properties relevant to cognitive modelling?

From the use of certain types of modelling (e.g. Bayesian modelling) in neuroscience, have we learned or can we hope to learn that the brain is a certain kind of machine (e.g. a Bayesian machine)?

How do model-based or mechanism-based approaches to cognitive neuroscience affect debates over reductionism, explanation and scientific realism?

Luigi Acerbi (University of Edinburgh)Synchronizing the Bayesian clock - a view on cognitive probabilistic modelling through time perception

Commentator: Evan Butts

13.00 – 14.00pm

Lunch (provided)

14.00 – 15.00pm

Francesca Rossi (University of Edinburgh)The role of anticipation in understanding cognition

Commentator: Richard Stöckle-Schobel

15.00 – 15.20pm

Tea/coffee break

15.20 – 16.40pm

Dr Mark Sprevak (University of Edinburgh)What is a computational mechanism?

Commentator: Orestis Palermos

16.40 – 17.30pm

General discussion

17.30 – Late

Discussion to be continued over drinks and conference dinner

Speakers and Abstracts

Mark Sprevak (University of Edinburgh)

What is a computational mechanism?

Recent philosophy of mind emphasises explanation of cognition by mechanistic, rather than law-based, models. A subclass of mechanistic models---computational models---seems to play a special role in this task. Cognitive science appears to explain our cognitive capacities by positing computational mechanisms implemented by the brain. But is there something special about this computational mode of mechanistic explanation? Is there a difference between computational mechanistic models and any other causal mechanistic model? In this paper, I examine what we mean when we attribute a computation to a physical system. I argue that computational mechanistic models take on a number of commitments that go beyond those of standard causal mechanistic explanations. In particular, I argue that any viable notion of computational implementation involves either a representational or an anti-realist component. I argue that these components have wide-reaching implications for computational mechanistic explanation in cognitive science, including naturalising cognition via computational mechanistic explanation, the thought that computational explanations are purely formal, and individualism in cognitive science.

Stephan Hartmann (University of Tilburg)

Models, mechanisms and coherence

Life-science phenomena are often explained by specifying and describing the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what a mechanisms are. While there is substantial disagreement among the new mechanists on various issues, they all share a commitment to scientific realism. But is such a commitment really necessary? In this talk, we propose an alternative antirealist account that also makes sense of the explanatory practice in the life sciences. To do so, we pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and the role of coherence considerations. To illustrate our points, we examine the development of a specific neuro-mechanism involving the action potential. The talk is based on joint work with Robert van Iersel (TiLPS).

Lena Kästner (Ruhr-University Bochum)

Mechanistic explanation and interventionism: An untruthful marriage

Scientiﬁc investigation into cognitive phenomena is not restricted to same-level (i.e. merely behavioral or merely neural) experimentation. Any serious attempt to make sense of the explanatory practices within cognitive science will therefore have to take cross-level experiments into account.

Mechanistic explanation seems to do justice to such cross-level empirical research. The interventionist account of causation—which mechanistic explanation is typically traded in a package deal with—does not keep this promise, however: within the interventionist framework, there is no plausible interpretation of the kinds of cross-level experiments so popular among empirical cognitive scientists. Yet, interventionism captures some core principles of experimental design: intervening into one process, to elicit changes in another (at a diﬀerent level) while “controlling for” background conditions.

Both interventionism and mechanistic explanation undoubtedly seem to have some epistemic import on our research into and understanding of cognitive phenomena. It is thus worth trying to save their marriage. I here consider possible strategies to resolve the tension arising between the two. However, I will ﬁnd that—as long as we buy the mechanists non-causal constitution relation as well as the interventionists postulate that she cashes out genuine causal relations—there does not seem to be a satisfactory solution.

Francesca Rossi (University of Edinburgh)

Role of anticipation in understanding cognition

Humans and other animals are not only able to coordinate their behaviors according to the current circumstances, but also with the future. Both are said to rely on anticipations, but cognitive agents show a broader range of capacities. I will first distinguish anticipation from behavioral conditioning. I will then argue that cognitive agents are not only able to overcome time-constraints, but are also flexible. Starting from the hypothesis that cognitive processes lead to good and correct performance (R. Cummins, 1995), I offer a possible way to arrive at a biological account of cognition by relying on the notion of anticipation as one of its essential elements. I will distinguish several different types of anticipation and I will show in which sense they permit flexible behaviors. I will then analyze and confront the Feedforward modeling approach, the Emulation Theory of Representation, the Bayesian and the Predictive coding modeling approaches and highlight the structure of their explanation and their accounts of anticipation in the light of Cummins’ hypothesis. I will suggest that a connection between probabilistic and control theory models could offer a way of accommodating a rational analysis with the idea that perception-action couplings are central in cognition, thus maintaining a focus on predictions about the possible internal cognitive architecture and an attention to biology.

Luigi Acerbi (University of Edinburgh)

Synchronizing the Bayesian clock - a view on cognitive probabilistic modelling through time perception

Considerations about simultaneity and synchronization of clocks famously led to the development of the theory of special relativity. Analogously, recent psychophysical experiments (C. Stetson et al., 2006) show that even for subjective time simultaneity is a relative, context-dependent concept. Time perception in human observers is prone to numerous, well-documented biases and illusions, including time-order reversal between events (D. Eagleman, 2008). Why do these temporal illusions occur? How does the brain synchronize different sensory stream?

Various hypotheses can be formulated and tested by taking a normative approach to perception, which formalizes the model space and provides the model-builder with a neat, powerful mathematical framework whose parameters can be ideally connected to the elements of a psychophysical experiment – although things in practice can be much harder. I will draw examples from my current theoretical and experimental work on time perception in order to discuss various points and possible insights about model building and alidation in psychophysics and cognitive science.